INSPECTr Project
Intelligence Network & Secure Platform for Evidence Correlation and Transfer
Project News
Intelligence Network & Secure Platform for Evidence Correlation and Transfer
Project News
18 deliverables in the INSPECTr project are classified for public
dissemination.
The remaining 62 deliverables are classified as confidential; i.e., only for
members of the consortium (including the Commission Services).
Each of the public deliverables
will be reviewed for dissemination to the public and will be hosted on the project website, if deemed
appropriate when complete.
Title
Iterative Learning for Semi-automatic Annotation Using User Feedback
Abstract
With the advent of state-of-the-art models based on Neural Networks,
the need for vast corpora of accurately labelled data has become fundamental.
However, building such datasets is a very resource-consuming task that
additionally requires domain expertise.
The present work seeks to alleviate this limitation by proposing an
interactive semi-automatic annotation tool using an incremental learning
approach to reduce human effort. The automatic models used to assist the
annotation are incrementally improved based on user corrections to
better annotate the next data.
To demonstrate the effectiveness of the proposed method, we build a dataset
with named entities and relations between them related to the crime field
with the help of the tool. Analysis results show that annotation effort is
considerably reduced while still maintaining the annotation quality compared
to fully manual labelling.
Authors:
Meryem Guemimi,
Daniel Camara
Center for Data Science, Judiciary Pôle of the French Gendarmerie,Pontoise, France
Ray Genoe
Centre for Cybersecurity and Cybercrime Investigation, University College Dublin, Dublin, Ireland
Publication:
The proceedings of the 4th International Conference on Intelligent Technologies and Applications
(INTAP 2021)
View
Title
LEA Capacity Building as a Driver for the Adoption of European Research
Abstract
The INSPECTr project aims to produce a proof of concept that will demonstrate solutions to many of the
issues faced by institutional procedures within law enforcement agencies (LEAs) for combating cybercrime.
Unlike many other H2020 projects, the results of INSPECTr will be freely available to stakeholders at the
end of the project, despite having a low technology readiness level. It is imperative that LEAs fully
understand the legal, security and ethical requirements for using disruptive and advanced technologies,
particularly with a platform that will provide AI assisted decision making, facilitate intelligence
gathering from online data sources and redefine how evidential data is discovered in other jurisdictions
and exchanged. However, INSPECTr will also require the support of stakeholders beyond the scope of the
project, in order to drive further development and investment towards market-readiness. The development
of a robust capacity building program has been included in the project to ensure that LEAs can confidently
use the system and that they fully understand both the pitfalls and the potential of the platform.
During our training needs analyses, various European instruments, standards and priorities are considered,
such as CEPOL’s EU Strategic Training Needs Assessment, the course development standards established by
ECTEG and Europol’s Training Competency Framework. With this research and through consultation with
internal and external stakeholders, we define the pathways of training for the INSPECTr platform in
which we aim to address the various roles in European LEAs and their requirements for the effective
delivery and assessment of the course. In keeping with the project’s ethics-by-design approach, the
training program produced by INSPECTr will have a strong emphasis on security and the fundamental
rights of citizens while addressing the gaps in capabilities and training within the EU LEA community.
In this paper we describe the process we apply to curriculum design, based on the findings of our
research and our continued engagement with LEA and technical partners throughout the life-cycle of the
project.
Authors:
Michael Whelan,
Ray Genoe
Centre for Cybersecurity and Cybercrime Investigation, University College Dublin, Ireland
Publication:
The proceedings of the CEPOL Research & Science Conference 2022,
Vilnius, Lithuania
View
Title
Developing of a Judicial Cases Cross-Check system for case searching and correlation using a standard for the Evidence.
Abstract
In a recent EU publication, a report commissioned by the European Union related to the Cross-border
Digital Criminal Justice environment, a set of specific business needs have been identified.
Some of the most relevant ones have been: i) the interoperability across different systems needs
to be ensured, ii) the stakeholders need to easily manage the data and ensure its quality, allowing
them to properly make use of it (e.g. use the data as evidence in a given case) and iii) the stakeholders
investigating a given case should be able to identify links between cross-border cases. Therefore,
solutions are needed to allow the stakeholder to search and find relevant information they need for
the case they are handling. The article presents a set of solutions to address the highlighted needs,
including a ‘Judicial Cases Cross-Check System’. Such a system should provide a tool being able to search
for case-related information and identify links among cases that are being investigated in other EU Member
States or by Justice and Home Affairs (JHA) agencies and EU bodies. To facilitate the development of the
above solution, a standard representation of the metadata and data of the evidence should be adopted.
In particular the Unified Cyber Ontology (UCO) and Cyber-investigation Analysis Standard Expression (CASE),
dedicated to the digital forensic domain, seem the most promising one to this aim. Moreover, it provides a
structured specification for representing information that are analysed and exchanged during investigations
involving digital evidence.
Authors:
Gerardo Giardiello,
Fabrizio Turchi
Institute of Legal Informatics and Judicial Systems, National Research Council of Italy (CNR-IGSG)
Publication:
The proceedings of the CEPOL Research & Science Conference 2022,
Vilnius, Lithuania
View
Title
Classification of Complaints for Criminal Intelligence Purposes
Abstract
The increase in the volume of available data is changing how people perceive their own fields and
how the people may interact with this surplus of information. Public security is not different; Law
Enforcement Agencies (LEAs) now have available a large quantity of information to help them fight
criminality. One challenging problem is to classify/predict criminal activities.
The differentiation over two different complaints may only be clear through the careful analysis of
complaints' open text fields, e.g., the modus operandi, where it is described the specificity of the
perpetrated crime. Sometimes the intention behind a crime is not evident unless it is correlated to other
crimes and patterns get extracted from them. This chapter shows that it is possible to classify criminal
data using machine learning-based methods and that open text fields, such as the modus operandi, may
play a fundamental role in the performance of the classification.
Authors:
Pauline Rousseau,
Dimitris Kotzinos
CY Cergy Paris University, France
Daniel Camara
Center for Data Science, Judiciary Pôle of the French Gendarmerie, Pontoise, France
Publication:
Book chapter in Applied Artificial Intelligence and Robotics for Government Processes
View
INSPECTr Project Coordinator (UCD-CCI)
UCD Centre for Cybersecurity and Cybercrime Investigation
UCD School of Computer Science
University College Dublin
Belfield, Dublin 4, Ireland
+353 1 716 2934
+353 1 716 2923